To overcome these limitations, we propose a novel approach which uses symbolic regression (SR) to automatically construct an analytic representation of the value function.
Symbolic regression has been used in nonlinear data-driven modeling with quite impressive results [1]}, [2]}, [3]}, [4]}.
To our best knowledge, there have been no reports in the literature on the use of symbolic regression for constructing value functions. The closest related research is the use of genetic programming for fitting already available V-functions [5]}, [6]}, which, however, is completely different from our approach.
